Add groupby.transform (only support for aggregations)

docs/cudf/source/api_docs/groupby.rst

@@ -34,6 +34,7 @@ Function application
    SeriesGroupBy.aggregate
    DataFrameGroupBy.aggregate
    GroupBy.pipe
+   GroupBy.transform
 
 Computations / descriptive stats
 --------------------------------

docs/cudf/source/basics/groupby.rst

@@ -1,3 +1,5 @@
+.. _basics.groupby:
+
 GroupBy
 =======
 
@@ -220,6 +222,27 @@ Limitations
  .. |describe| replace:: ``describe``
  .. _describe: https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#flexible-apply
 
+
+Transform
+---------
+
+The ``.transform()`` method aggregates per group, and broadcasts the
+result to the group size, resulting in a Series/DataFrame that is of
+the same size as the input Series/DataFrame.
+
+.. code:: python
+
+     >>> import cudf
+     >>> df = cudf.DataFrame({'a': [2, 1, 1, 2, 2], 'b': [1, 2, 3, 4, 5]})
+     >>> df.groupby('a').transform('max')
+        b
+     0  5
+     1  3
+     2  3
+     3  5
+     4  5
+
+
 Rolling window calculations
 ---------------------------
 

python/cudf/cudf/core/groupby/groupby.py

@@ -184,11 +184,25 @@ def agg(self, func):
         Parameters
         ----------
         func : str, callable, list or dict
+            Argument specifying the aggregation(s) to perform on the
+            groups. `func` can be any of the following:
+
+              - string: the name of a supported aggregation
+              - callable: a function that accepts a Series/DataFrame and
+                performs a supported operation on it.
+              - list: a list of strings/callables specifying the
+                aggregations to perform on every column.
+              - dict: a mapping of column names to string/callable
+                specifying the aggregations to perform on those
+                columns.
+
+        See :ref:`the user guide <basics.groupby>` for supported
+        aggregations.
 
         Returns
         -------
         A Series or DataFrame containing the combined results of the
-        aggregation.
+        aggregation(s).
 
         Examples
         --------
@@ -655,6 +669,54 @@ def rolling_avg(val, avg):
         kwargs.update({"chunks": offsets})
         return grouped_values.apply_chunks(function, **kwargs)
 
+    def transform(self, function):
+        """Apply an aggregation, then broadcast the result to the group size.
+
+        Parameters
+        ----------
+        function: str or callable
+            Aggregation to apply to each group. Note that the set of
+            operations currently supported by `transform` is identical
+            to that supported by the `agg` method.
+
+        Returns
+        -------
+        A Series or DataFrame of the same size as the input, with the
+        result of the aggregation per group broadcasted to the group
+        size.
+
+        Examples
+        --------
+        .. code-block:: python
+
+          import cudf
+          df = cudf.DataFrame({'a': [2, 1, 1, 2, 2], 'b': [1, 2, 3, 4, 5]})
+          df.groupby('a').transform('max')
+             b
+          0  5
+          1  3
+          2  3
+          3  5
+          4  5
+
+        See also
+        --------
+        cudf.core.groupby.GroupBy.agg
+        """
+        try:
+            result = self.agg(function)
+        except TypeError as e:
+            raise NotImplementedError(
+                "Currently, `transform()` supports only aggregations."
+            ) from e
+
+        if not result.index.equals(self.grouping.keys):
+            result = result._align_to_index(
+                self.grouping.keys, how="right", allow_non_unique=True
+            )
+            result = result.reset_index(drop=True)
+        return result
+
     def rolling(self, *args, **kwargs):
         """
         Returns a `RollingGroupby` object that enables rolling window

python/cudf/cudf/tests/test_groupby.py

@@ -2362,6 +2362,28 @@ def test_groupby_get_group(pdf, group, name, obj):
     assert_groupby_results_equal(expected, actual)
 
 
+@pytest.mark.parametrize(
+    "by",
+    [
+        "a",
+        ["a", "b"],
+        pd.Series([2, 1, 1, 2, 2]),
+        pd.Series(["b", "a", "a", "b", "b"]),
+    ],
+)
+@pytest.mark.parametrize("agg", ["sum", "mean", lambda df: df.mean()])
+def test_groupby_transform_aggregation(by, agg):
+    gdf = cudf.DataFrame(
+        {"a": [2, 2, 1, 2, 1], "b": [1, 1, 1, 2, 2], "c": [1, 2, 3, 4, 5]}
+    )
+    pdf = gdf.to_pandas()
+
+    expected = pdf.groupby(by).transform(agg)
+    actual = gdf.groupby(by).transform(agg)
+
+    assert_groupby_results_equal(expected, actual)
+
+
 def test_groupby_select_then_ffill():
     pdf = pd.DataFrame(
         {